A miniaturized radio signal visualization monitoring, positioning and identification system and method
By analyzing the historical records of radio monitoring stations to calculate positioning priorities and selecting appropriate combinations of monitoring stations, the problem of difficult allocation of multiple signal sources was solved, enabling accurate positioning and efficient monitoring of abnormal signal sources.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- QINGDAO ZHENGXIN IND CONTROL TECH
- Filing Date
- 2024-09-23
- Publication Date
- 2026-05-26
AI Technical Summary
Existing radio monitoring stations struggle to efficiently capture anomalous signal sources that frequently change location, especially when multiple signal sources are present, requiring coordination among multiple stations, which makes signal source allocation difficult.
By analyzing the historical monitoring records of multiple monitoring stations, the positioning priority data for each monitoring direction is calculated. The monitoring station with the highest positioning priority is selected as the first monitoring station, and auxiliary monitoring stations are selected to assign target monitoring tasks to achieve accurate positioning.
It improved the accuracy and efficiency of monitoring abnormal signal sources and enabled the rational allocation and precise positioning of multiple monitoring stations.
Smart Images
Figure CN119199725B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of radio monitoring, and in particular to a miniaturized radio signal visualization monitoring, positioning and identification system and method. Background Technology
[0002] Currently, radio monitoring stations are generally used to monitor radio signals. These stations can detect, locate, and identify abnormal radio signal sources in any direction. When monitoring in any direction, the station simultaneously records the monitoring direction and data. Since radio monitoring stations of the same specifications have the same coverage area in the same monitoring direction, the recorded monitoring direction can be used to determine if there is any overlap in the coverage areas of any two monitoring stations. To detect and identify abnormal radio signal sources in another direction, the monitoring equipment needs to be rotated. Generally, abnormal signal sources frequently change location, making it difficult to detect them with a single monitoring station. If multiple abnormal signal sources appear, multiple radio monitoring stations need to work together to detect them. Therefore, the allocation of radio monitoring stations for abnormal signal sources is crucial. Summary of the Invention
[0003] This application provides a miniaturized radio signal visualization monitoring, positioning and identification system and method, which can capture abnormal signal sources by allocating multiple radio monitoring stations, which is beneficial for the accurate positioning of abnormal signal sources.
[0004] In a first aspect, this application provides a miniaturized radio signal visualization monitoring, positioning, and identification system. The system includes multiple monitoring stations and a server, the server being configured with the following method:
[0005] Acquire historical monitoring records for each monitoring station within a preset time period; the historical monitoring records include the azimuth and quantity data of historical abnormal signal sources corresponding to the historical monitoring direction;
[0006] Based on the analysis of the historical monitoring records, the original number of historical abnormal signal sources and the original location of each historical abnormal signal source in each historical monitoring direction are determined; the original location is the location data of the first occurrence of each historical abnormal signal source within a preset time period; the original number is the number of abnormal signal sources captured by the monitoring station when it first performs monitoring work in any historical monitoring direction within the preset time period.
[0007] Based on the original orientation, original quantity, and historical monitoring records, analyze the orientation change data and quantity change data for each historical monitoring direction and each historical abnormal signal source; the orientation change data is the number of orientation changes for each historical abnormal signal source; the quantity change data includes quantity increase data and quantity decrease data.
[0008] The positioning priority data for each historical monitoring direction is calculated based on the azimuth change data and quantity change data; the positioning priority data represents the degree of coordination priority of each monitoring station for each historical monitoring direction.
[0009] Furthermore, the server is further configured to, after obtaining the location priority data, also include:
[0010] The monitoring station with the highest positioning priority data is designated as the first monitoring station;
[0011] Two monitoring stations whose positioning priority data is not greater than a preset priority threshold are selected as auxiliary monitoring stations;
[0012] The target monitoring task is determined based on the first monitoring station; the target monitoring task is to perform monitoring work on the historical monitoring direction with the highest positioning priority data according to the first monitoring station.
[0013] The target monitoring task was assigned to two auxiliary monitoring stations.
[0014] Furthermore, the server is further configured such that the calculation of positioning priority data for each historical monitoring direction based on the azimuth change data and quantity change data includes:
[0015] Calculate the basic impact data based on the original quantities;
[0016] Calculate the azimuth influence data based on the azimuth change data;
[0017] Calculate the quantity impact data based on the quantity change data;
[0018] Based on the basic impact data, the azimuth impact data, and the quantity impact data, the positioning priority data for each historical monitoring direction is calculated.
[0019] Furthermore, the server is further configured such that the method for calculating the location priority data includes:
[0020] S = B × K1 + P × K2 + M × K3
[0021] In the formula, S is the positioning priority data, B is the basic influence data, P is the orientation influence data, and M is the quantity influence data; K1, K2, and K3 are the first preset weight, the second preset weight, and the third preset weight, respectively.
[0022] Furthermore, the server is further configured such that K1+K2+K3=1.
[0023] Secondly, this application provides a miniaturized method for visual monitoring, location, and identification of radio signals, the method comprising:
[0024] Acquire historical monitoring records for each monitoring station within a preset time period; the historical monitoring records include the azimuth and quantity data of historical abnormal signal sources corresponding to the historical monitoring direction;
[0025] Based on the analysis of the historical monitoring records, the original number of historical abnormal signal sources and the original location of each historical abnormal signal source in each historical monitoring direction are determined; the original location is the location data of the first occurrence of each historical abnormal signal source within a preset time period; the original number is the number of abnormal signal sources captured by the monitoring station when it first performs monitoring work in any historical monitoring direction within the preset time period.
[0026] Based on the original orientation, original quantity, and historical monitoring records, analyze the orientation change data and quantity change data for each historical monitoring direction and each historical abnormal signal source; the orientation change data is the number of orientation changes for each historical abnormal signal source; the quantity change data includes quantity increase data and quantity decrease data.
[0027] The positioning priority data for each historical monitoring direction is calculated based on the azimuth change data and quantity change data; the positioning priority data represents the degree of coordination priority of each monitoring station for each historical monitoring direction.
[0028] Furthermore, after obtaining the positioning priority data, the method further includes:
[0029] The monitoring station with the highest positioning priority data is designated as the first monitoring station;
[0030] Two monitoring stations whose positioning priority data is not greater than a preset priority threshold are selected as auxiliary monitoring stations;
[0031] The target monitoring task is determined based on the first monitoring station; the target monitoring task is to perform monitoring work on the historical monitoring direction with the highest positioning priority data according to the first monitoring station.
[0032] The target monitoring task was assigned to two auxiliary monitoring stations.
[0033] Furthermore, the calculation of the positioning priority data for each historical monitoring direction based on the azimuth change data and quantity change data includes:
[0034] Calculate the basic impact data based on the original quantities;
[0035] Calculate the azimuth influence data based on the azimuth change data;
[0036] Calculate the quantity impact data based on the quantity change data;
[0037] Based on the basic impact data, the azimuth impact data, and the quantity impact data, the positioning priority data for each historical monitoring direction is calculated.
[0038] Furthermore, the method for calculating the positioning priority data includes:
[0039] S = B × K1 + P × K2 + M × K3
[0040] In the formula, S is the positioning priority data, B is the basic influence data, P is the orientation influence data, and M is the quantity influence data; K1, K2, and K3 are the first preset weight, the second preset weight, and the third preset weight, respectively.
[0041] Furthermore, K1+K2+K3=1.
[0042] It should be understood that the description in the Summary Section is not intended to limit the key or essential features of the embodiments of this application, nor is it intended to restrict the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description
[0043] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:
[0044] Figure 1 A flowchart illustrating a server execution method in a miniaturized radio signal visualization monitoring and positioning identification system according to an embodiment of this application is shown. Detailed Implementation
[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0046] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0047] This application provides a miniaturized radio signal visualization monitoring, positioning and identification system and method, which can more accurately locate abnormal signal sources.
[0048] refer to Figure 1 In a first aspect, this application provides a miniaturized radio signal visualization monitoring, positioning, and identification system. The system includes multiple monitoring stations and a server. The server is configured with the following methods: acquiring historical monitoring records for each monitoring station within a preset time period; the historical monitoring records include azimuth data and quantity data of historical abnormal signal sources corresponding to the historical monitoring direction; analyzing and determining the original quantity and original azimuth of historical abnormal signal sources in each historical monitoring direction based on the historical monitoring records; the original azimuth being the location data of the first occurrence of each historical abnormal signal source within the preset time period; the original quantity being the quantity data of abnormal signal sources captured by the monitoring station when it first performs monitoring work in any historical monitoring direction within the preset time period; analyzing the azimuth change data and quantity change data of each historical monitoring direction and each historical abnormal signal source based on the original azimuth, original quantity, and the historical monitoring records; the azimuth change data being the number of times the azimuth of each historical abnormal signal source changes; the quantity change data including quantity increase data and quantity decrease data; calculating the positioning priority data for each historical monitoring direction based on the azimuth change data and quantity change data; the positioning priority data representing the cooperation priority degree of each monitoring station in each historical monitoring direction.
[0049] In this system, all monitoring stations have the same specifications, and any two monitoring stations can cooperate to achieve an overlap in their monitoring coverage in one monitoring direction. Any three monitoring stations can cooperate to accurately locate abnormal signal sources within the overlapping range of their monitoring coverage.
[0050] It is understandable that the monitoring direction here refers to the monitoring equipment of the monitoring station. The direction in which the monitoring equipment faces determines the direction it can receive abnormal radio signals within a certain distance and range in that direction, thereby capturing the source of the abnormal signal. Therefore, the monitoring direction mentioned in this scheme is the conventional monitoring direction that conforms to the performance and working nature of the monitoring equipment. The monitoring direction recorded in the historical monitoring records is based on the orientation of the monitoring equipment. In this way, the monitoring direction in the historical monitoring records of each monitoring station can be accurately located. For example, the historical monitoring records show that the monitoring equipment of monitoring station A faces southeast, and the monitoring equipment of monitoring station B faces northwest, etc.
[0051] Understandably, in actual monitoring work, monitoring stations typically adjust the orientation of their monitoring equipment periodically. Consequently, historical monitoring records may show a single station monitoring multiple historical directions, thus revealing the location and number of historical anomalous signal sources detected. The location data for these historical anomalous signal sources generally refers to the direction and approximate distance of the signal source, such as more than 200 meters to the southeast. Because a single monitoring station can only monitor the direction and approximate distance of the signal source, it cannot pinpoint its exact location. Therefore, three monitoring stations are needed to coordinate their efforts, as three straight lines can determine an accurate point in space. This solution analyzes the historical monitoring records of each station to determine the situation of historical anomalous signal sources in the corresponding historical monitoring direction for each station, thereby identifying the historical anomalous signal sources with higher priority that require urgent and precise location.
[0052] It is understandable that the historical monitoring direction mentioned in the scheme of this application is bound to each monitoring station, such as the southeast direction of monitoring station A. Therefore, after obtaining the historical monitoring records, it is still necessary to conduct a specific analysis of the historical monitoring records.
[0053] In the scheme of this application, historical monitoring records within a preset time period are analyzed. The preset time period is generally the current time node as the end point of the time period, and historical monitoring records within the preset time period close to the current time node are selected. Since the abnormal signal source will frequently change its location, it is difficult to guarantee that the abnormal signal source can be captured in time even if a processing strategy is taken in time when the abnormal signal source is detected. Therefore, it is necessary to continuously monitor the abnormal signal source. Thus, there are multiple abnormal signal sources that have not been captured within the preset time period. By analyzing the changes in the number and location of the abnormal signal sources, the monitoring priority of the abnormal signal sources in that monitoring direction can be determined.
[0054] In actual monitoring work, when an abnormal signal source is detected, it will be marked so that it can be identified when it reappears. Therefore, the original number of historical abnormal signal sources in each historical monitoring direction and the original location of each historical abnormal signal source can be determined through historical monitoring records.
[0055] Once the original quantity and original orientation are obtained, the quantity change data and orientation change data can be determined.
[0056] Specifically, the analysis method for quantity change data here is as follows: for any historical monitoring direction in the historical monitoring records, the quantity difference is obtained by subtracting the minimum quantity from the maximum quantity recorded in the historical monitoring records for that historical monitoring direction; then, the basic difference is obtained by subtracting the original quantity from the last recorded quantity within the preset time period; the quantity change data equals the basic difference multiplied by the quantity difference. It can be understood that for the number of abnormal signal sources appearing in that historical monitoring direction, if the last recorded quantity is less than the original quantity, it indicates that an abnormal signal source has been lost; otherwise, it indicates that an abnormal signal source has increased. If an abnormal signal source is lost, the priority of that historical monitoring direction will decrease; if an abnormal signal source increases, the priority of that historical monitoring direction will increase. Specifically, the quantity change data is calculated as follows: m = d1 × d2; where m is the quantity change data, d1 is the basic difference, and d2 is the quantity difference.
[0057] The azimuth change data here refers to the number of azimuth changes for each historical anomalous signal source. Specifically, suppose that n historical anomalous signal sources appeared in the historical monitoring direction 'a' of monitoring station A within a preset time period; and the number of azimuth changes for each historical anomalous signal source is c. i Then the azimuth change data In the formula, p represents the azimuth change data.
[0058] In this embodiment of the application, after obtaining the azimuth change data and the quantity change data, the positioning priority data can be calculated. Specifically, calculating the positioning priority data for each historical monitoring direction based on the azimuth change data and the quantity change data includes: calculating basic influence data based on the original quantity; calculating azimuth influence data based on the azimuth change data; calculating quantity influence data based on the quantity change data; and calculating the positioning priority data for each historical monitoring direction based on the basic influence data, the azimuth influence data, and the quantity influence data.
[0059] Specifically, the calculation methods for basic impact data include: B = o × k1 + b1; where B is the basic impact data, o is the original quantity, k1 is the preset first conversion coefficient, and b1 is the preset first correction coefficient; the calculation methods for azimuth impact data include: P = p × k2 + b2; where P is the azimuth impact data, p is the azimuth change data, k2 is the preset second conversion coefficient, and b2 is the preset second correction coefficient; the calculation methods for quantity impact data include: M = m × k3 + b3; where M is the quantity impact data, m is the quantity change data, k3 is the preset third conversion coefficient, and b3 is the preset third correction coefficient.
[0060] Furthermore, the method for calculating location priority data includes:
[0061] S = B × K1 + P × K2 + M × K3
[0062] In the formula, S is the positioning priority data, B is the basic influence data, P is the orientation influence data, and M is the quantity influence data; K1, K2, and K3 are the first preset weight, the second preset weight, and the third preset weight, respectively; where K1+K2+K3=1.
[0063] In the scheme of this application, after obtaining the positioning priority data, the monitoring station with the highest positioning priority data is determined as the first monitoring station; two monitoring stations with positioning priority data not greater than a preset priority threshold are selected as auxiliary monitoring stations; a target monitoring task is determined based on the first monitoring station; the target monitoring task is to perform monitoring work on the historical monitoring direction with the highest positioning priority data of the first monitoring station; and the target monitoring task is assigned to the two auxiliary monitoring stations.
[0064] It is understandable that, as mentioned above, each historical monitoring direction is bound to a monitoring station. Therefore, after calculating the positioning priority data for each historical monitoring direction, the auxiliary monitoring station needs to consider the relative position of its auxiliary monitoring station and the first monitoring station when adjusting the monitoring direction, based on the historical monitoring direction of the corresponding monitoring station.
[0065] It is understood that the method running within the server in this application is actually a machine learning model. By continuously calculating the positioning priority data, the corresponding parameters are continuously trained. The trainable parameters include the first preset weight, the second preset weight, the third preset weight, the preset first conversion coefficient, the preset first correction coefficient, the preset second conversion coefficient, the preset second correction coefficient, the preset third conversion coefficient, and the preset third correction coefficient. By continuously training the above parameters, the calculation result of the positioning priority data becomes more accurate, thereby achieving a more reasonable and accurate allocation of monitoring work for monitoring stations and improving the accuracy of monitoring abnormal signal sources.
[0066] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.
[0067] Secondly, this application provides a miniaturized method for visual monitoring, positioning, and identification of radio signals. This method is applied to a server; specifically, it includes: acquiring historical monitoring records for each monitoring station within a preset time period; the historical monitoring records include azimuth and quantity data of historical anomalous signal sources corresponding to the historical monitoring direction; analyzing and determining the original quantity and original azimuth of historical anomalous signal sources in each historical monitoring direction based on the historical monitoring records; the original azimuth being the location data of the first occurrence of each historical anomalous signal source within the preset time period; the original quantity being the quantity data of anomalous signal sources captured by the monitoring station when it first performs monitoring work in any historical monitoring direction within the preset time period; analyzing the azimuth change data and quantity change data for each historical monitoring direction and each historical anomalous signal source based on the original azimuth, original quantity, and the historical monitoring records; the azimuth change data being the number of times the azimuth of each historical anomalous signal source changes; the quantity change data including quantity increase data and quantity decrease data; calculating the positioning priority data for each historical monitoring direction based on the azimuth change data and quantity change data; the positioning priority data representing the cooperation priority degree of each monitoring station in each historical monitoring direction.
[0068] Furthermore, after obtaining the positioning priority data, the method further includes: determining the monitoring station with the highest positioning priority data as the first monitoring station; selecting two monitoring stations with positioning priority data not greater than a preset priority threshold as auxiliary monitoring stations; determining a target monitoring task based on the first monitoring station; the target monitoring task is to perform monitoring work on the historical monitoring direction with the highest positioning priority data of the first monitoring station; and allocating the target monitoring task to the two auxiliary monitoring stations.
[0069] Furthermore, the step of calculating the positioning priority data for each historical monitoring direction based on the azimuth change data and the quantity change data includes: calculating basic impact data based on the original quantity; calculating azimuth impact data based on the azimuth change data; calculating quantity impact data based on the quantity change data; and calculating the positioning priority data for each historical monitoring direction based on the basic impact data, the azimuth impact data, and the quantity impact data.
[0070] Furthermore, the method for calculating the positioning priority data includes:
[0071] S = B × K1 + P × K2 + M × K3
[0072] In the formula, S is the positioning priority data, B is the basic influence data, P is the orientation influence data, and M is the quantity influence data; K1, K2, and K3 are the first preset weight, the second preset weight, and the third preset weight, respectively; where K1+K2+K3=1.
[0073] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the described device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0074] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.
Claims
1. A miniaturized radio signal visualization monitoring, positioning, and identification system, comprising multiple monitoring stations and a server, characterized in that, The server is configured with the following method: acquiring historical monitoring records of each monitoring station within a preset time period; the historical monitoring records include the azimuth data and quantity data of historical abnormal signal sources corresponding to the historical monitoring direction; Based on the analysis of the historical monitoring records, the original number of historical abnormal signal sources in each historical monitoring direction and the original location of each historical abnormal signal source are determined. The original orientation is the location data of the first occurrence of each historical abnormal signal source within a preset time period; The original quantity is the number of abnormal signal sources captured by the monitoring station when it first performs monitoring work in any historical monitoring direction within a preset time period; Based on the original orientation, original quantity, and historical monitoring records, analyze the orientation change data and quantity change data for each historical monitoring direction and each historical abnormal signal source; the orientation change data is the number of orientation changes for each historical abnormal signal source; the quantity change data includes quantity increase data and quantity decrease data. The calculation method for the quantity change data is: m = d1 × d2; where m is the quantity change data, d1 is the basic difference, and d2 is the quantity difference; wherein, the quantity difference is obtained by subtracting the minimum quantity from the maximum quantity recorded in the historical monitoring record for that historical monitoring direction, and the basic difference is obtained by subtracting the original quantity from the last recorded quantity within the preset time period; the azimuth change data is: ; In the formula, n is the number of historical anomalous signal sources, and c i This represents the number of directional changes. The positioning priority data for each historical monitoring direction is calculated based on the azimuth change data and quantity change data; the positioning priority data represents the degree of coordination priority of each monitoring station in each historical monitoring direction; The server is further configured to, after obtaining the positioning priority data, also include: determining the monitoring station with the highest positioning priority data as the first monitoring station; Two monitoring stations whose positioning priority data is not greater than a preset priority threshold are selected as auxiliary monitoring stations; The target monitoring task is determined based on the first monitoring station; the target monitoring task is to perform monitoring work on the historical monitoring direction with the highest positioning priority data according to the first monitoring station. The target monitoring task was assigned to two auxiliary monitoring stations; The server is further configured such that the calculation of positioning priority data for each historical monitoring direction based on the azimuth change data and quantity change data includes: calculating basic impact data based on the original quantity; Calculate the azimuth influence data based on the azimuth change data; Calculate the quantity impact data based on the quantity change data; The calculation method for the basic impact data includes B = o × k_1 + b_1; In the formula, B is the basic influence data, o is the original quantity, k_1 is the preset first transformation coefficient, and b_1 is the preset first correction coefficient; The calculation method for the azimuth influence data includes P = p × k_2 + b_2; In the formula, P represents the azimuth influence data, p represents the azimuth change data, k_2 represents the preset second conversion coefficient, and b_2 represents the preset second correction coefficient; The calculation method for the quantity influence data includes M = m × k_3 + b_3; In the formula, M represents the quantity influence data, m represents the quantity change data, k_3 represents the preset third conversion coefficient, and b_3 represents the preset third correction coefficient; Based on the basic impact data, the azimuth impact data, and the quantity impact data, calculate the positioning priority data for each historical monitoring direction; The server is further configured such that the method for calculating the location priority data includes: S = B × K1 + P × K2 + M × K3; In the formula, S is the positioning priority data, B is the basic influence data, P is the orientation influence data, and M is the quantity influence data; K1, K2, and K3 are the first preset weight, the second preset weight, and the third preset weight, respectively.
2. The miniaturized radio signal visualization monitoring, positioning, and identification system according to claim 1, characterized in that, The server is further configured such that K1+K2+K3=1.
3. A miniaturized method for visual monitoring, positioning, and identification of radio signals, applied to the server described in claim 1 or 2, characterized in that, include: Acquire historical monitoring records for each monitoring station within a preset time period; the historical monitoring records include the azimuth and quantity data of historical abnormal signal sources corresponding to the historical monitoring direction; Based on the analysis of the historical monitoring records, the original number of historical abnormal signal sources in each historical monitoring direction and the original location of each historical abnormal signal source are determined. The original orientation is the location data of the first occurrence of each historical abnormal signal source within a preset time period; The original quantity is the number of abnormal signal sources captured by the monitoring station when it first performs monitoring work in any historical monitoring direction within a preset time period; Based on the original orientation, original quantity, and historical monitoring records, analyze the orientation change data and quantity change data for each historical monitoring direction and each historical abnormal signal source; the orientation change data is the number of orientation changes for each historical abnormal signal source; the quantity change data includes quantity increase data and quantity decrease data. The positioning priority data for each historical monitoring direction is calculated based on the azimuth change data and quantity change data; the positioning priority data represents the degree of coordination priority of each monitoring station for each historical monitoring direction.